Developing and deploying a use-inspired metapopulation framework for stratified health outcomes

People involved

Roadmap

Motivation

• Public health decision makers increasingly rely on epidemiological models to:
  • Forecast likely futures
  • Stress-test interventions
  • Plan resource allocation (e.g., beds, staffing)

Motivation

Motivation

MetaRVM - Disease model

MetaRVM - Population mixing

• Allows for subpopulations based on demographic characteristics, e.g., based on age: children (green boxes), working-age adults (blue boxes), and seniors (orange boxes)
• Allows for different mixing patterns for daytime and nighttime: obtained through social mixing studies or through synthetic population schedules

Mixing and force of infection

• For each stratum, the force of infection depends on:
  • Mixing rates between the strata \(M = (m_{ij})\)
  • Prevalence of infectious individuals in \(j\)th stratum at time \(t\), \(I_t^{(j)}\), where \(j \in \mathcal{J}\)

Mixing and force of infection

• Effective interacting population (exclude Hospitalized [H], Dead [D] from total Population [P])

\[ \mathrm{MP}_t^{(j)} = P_t^{(j)} - H_t^{(j)} - D_t^{(j)}, \qquad j \in \mathcal{J}. \]

• Mixing across demographic strata

\(M\): mixing matrix \[ M = (m_{ij}), \qquad \sum_{j \in \mathcal{J}} m_{ij} = 1 \ \forall i, \]

\[ \mathrm{MP}_{t,\text{eff}}^{(j)} = \sum_{i \in \mathcal{J}} m_{ij}\,\mathrm{MP}_t^{(i)}, \quad S_{t,\text{eff}}^{(j)} = \sum_{i \in \mathcal{J}} m_{ij}\,S_t^{(i)}, \quad I_{t,\text{eff}}^{(j)} = \sum_{i \in \mathcal{J}} m_{ij}\,I_t^{(i)}. \]

Mixing and force of infection

• Force of infection (Susceptible [S] vs Vaccinated [V])

\[ \lambda_{s,t}^{(j)} = \beta_s \frac{I_{t,\text{eff}}^{(j)}}{\mathrm{MP}_{t,\text{eff}}^{(j)}}, \qquad \lambda_{v,t}^{(j)} = \beta_v \frac{I_{t,\text{eff}}^{(j)}}{\mathrm{MP}_{t,\text{eff}}^{(j)}}. \]

• S → Exposed [E] transition in stratum (j)

\[ p_{\mathrm{SE}}^{(j)} = 1 - \exp\!\big(-\lambda_{s,t}^{(j)} \,\Delta t\big), \\ \Delta SE_t^{(j)} = p_{\mathrm{SE}}^{(j)}\,S_t^{(j)}. \]

Age-stratified model - illustration

Ingredients

Age-stratified model - illustration

Age-stratified model - illustration

Mobility –> Mixing

Daytime vs Nightime mixing

MetaRVM in action

Prepare configuration

run_id: age-stratified
population_data:
  mapping: demographic_mapping.csv
  initialization: population_init.csv
  vaccination: vaccination.csv
mixing_matrix:
  weekday_day: m_wd_d.csv
  weekday_night: m_wd_n.csv
  weekend_day: m_we_d.csv
  weekend_night: m_we_n.csv
disease_params:
  ts: 0.5
  tv: 0.3
  ve : 0.5
  dv: 158
  dp: 1
  de: 3
  da: 5
  ds: 6
  dh: 4
  dr: 187
  pea: 0.333
  psr: 0.95
  phr: 0.97
sub_disease_params:
    age:
      0-17:
        ts: 0.5
        tv: 0.1
        pea: 0.1
        psr: 0.99
      18-64:
        ts: 0.5
        tv: 4
        pea: 0.1
        psr: 0.99
      65+:
        ts: 0.5
        tv: 7
        pea: 0.3
        psr: 0.97
simulation_config:
  start_date: 10/01/2023 # m/d/Y
  length: 200

MetaRVM in action

Run simulation

library(MetaRVM)
out <- metaRVM("config.yaml")

MetaRVM in action

Use of checkpoints

out <- metaRVM("config_checkpoint.yaml")

Tracking age-stratified influenza hospitalizations

• Goal:
  • Calibrate MetaRVM to weekly hospitalizations
  • Parameters to estimate:
    • \(ts\): transmissibility for susceptible population
    • \(tv\): transmissibility for vaccinated population
    • \(pea\): proportion of asymptomatic cases,
    • \(psh\): proportion of hospitalized among symptomatic cases
• Approach:
  • Apply Bayesian Optimization to find relevant trajectories matching the observed data (Fadikar et al. (2023))

Tracking age-stratified influenza hospitalizations

Summary

• MetaRVM provides a use-inspired metapopulation modeling framework for:
  • Detailed, stratified tracking of health outcomes
  • Infectious disease spread across multiple interacting subpopulations
• Key contributions:
  • Uses synthetic population-derived mixing matrices
  • Balances realism with computational efficiency
  • Delivered as an open-source R package with a Shiny front end
• Case study:
  • Demonstrated on influenza-related hospitalizations in Chicago
  • Supports prospective surveillance and scenario analysis

Current and Future Work

• Extend MetaRVM to other respiratory diseases of public health concern (COVID, RSV) and additional demographic characteristics (geographic, race/ethnicity)
• Apply checkpoint-aware Bayesian optimization
• Utilize moving windows for improved trend detection
• Expand nowcasting capabilities to forecasting and scenario analyses

Funding acknowledgments

• Chicago Department of Public Health